https://github.com/jshilong/gpt4roi
공식 코드GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest
Papers with Code 아카이브
Visual Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 30
- 보존된 평가 기록
- 334
MLLM Leaderboard
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jsuarez5341/Efficient-Dynamic-Batching
공식 코드Effective Approaches to Batch Parallelization for Dynamic Neural Network Architectures
https://github.com/justairr/satori-r1
공식 코드SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and Verifiable Rewards
https://github.com/jxliu-ai/medcot
공식 코드https://github.com/jzy-123/biod2c
공식 코드BioD2C: A Dual-level Semantic Consistency Constraint Framework for Biomedical VQA
https://github.com/kaavyarekanar/towards-a-performance-analysis-on-pre-trained-vqa-models-for-autonomous-driving
공식 코드https://github.com/kai-wen-yang/qvix
공식 코드https://github.com/kaist-cvml/3d-vlm-gd
공식 코드3D-Aware Vision-Language Models Fine-Tuning with Geometric Distillation
https://github.com/kaistai/volcano
공식 코드Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision
https://github.com/kangreen0210/lime
공식 코드https://github.com/kangreen0210/lime-m
공식 코드https://github.com/kasuba-badri-vishal/dhrishtikon
공식 코드DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images
https://github.com/katiefraser/pairs
공식 코드https://github.com/kesenzhao/uv-cot
공식 코드Unsupervised Visual Chain-of-Thought Reasoning via Preference Optimization
https://github.com/khanhnguyen21006/mia_docvqa
공식 코드DocMIA: Document-Level Membership Inference Attacks against DocVQA Models
https://github.com/kkahatapitiya/langrepo
공식 코드https://github.com/knowlab/halt-medvqa
공식 코드Hallucination Benchmark in Medical Visual Question Answering
https://github.com/knowledgetechnologyuhh/grid-3d
공식 코드https://github.com/koen-47/columbus
공식 코드COLUMBUS: Evaluating COgnitive Lateral Understanding through Multiple-choice reBUSes
https://github.com/kovvalsky/spacenli
공식 코드SpaceNLI: Evaluating the Consistency of Predicting Inferences in Space
https://github.com/kraina-ai/geospatial-code-llms-dataset
공식 코드https://github.com/kshitij98/cvil
공식 코드cViL: Cross-Lingual Training of Vision-Language Models using Knowledge Distillation
https://github.com/kuan2jiu99/audio-hallucination
공식 코드https://github.com/kugwzk/dide
공식 코드Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/kugwzk/distilled-dualencoder
공식 코드Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/kushalkafle/DVQA_dataset
공식 코드DVQA: Understanding Data Visualizations via Question Answering
https://github.com/kushalkafle/PREFIL
공식 코드Answering Questions about Data Visualizations using Efficient Bimodal Fusion
https://github.com/kvt0012/viclevr
공식 코드https://github.com/kyleseelman/vqa_accessibility
공식 코드https://github.com/kyrochi/qlip
공식 코드QLIP: A Dynamic Quadtree Vision Prior Enhances MLLM Performance Without Retraining
https://github.com/kzampog/cilantro
공식 코드cilantro: A Lean, Versatile, and Efficient Library for Point Cloud Data Processing
https://github.com/lab-rasool/awesome-medical-vlms-and-datasets
공식 코드Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review
https://github.com/lackel/dka
공식 코드https://github.com/lalithjets/surgical_vqa
공식 코드Surgical-VQA: Visual Question Answering in Surgical Scenes using Transformer
https://github.com/lalithjets/surgicalgpt
공식 코드SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgery
https://github.com/lamalab-org/mac-bench
공식 코드Probing the limitations of multimodal language models for chemistry and materials research
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.